Centroidal Voronoi tessellations (CVTs) are special Voronoi
tessellations whose generators are also the centers of mass
(centroids) of the Voronoi regions with respect to a given density
function and CVT-based methodologies have been proven to be very
useful in many diverse applications in science and engineering. In
the context of image processing and its simplest form, CVT-based
algorithms reduce to the well-known k-means clustering and are
easy to implement. In this talk, we present an edge-weighted
centroidal Voronoi tessellation (EWCVT) model for image
segmentation and some efficient algorithms for its
construction. Our EWCVT model can overcome some deficiencies
possessed by the basic CVT model; in particular, the new model
appropriately combines the image intensity information together with
the length of cluster boundaries, and can handle very sophisticated
situations. We demonstrate through extensive examples the
efficiency, effectiveness, robustness, and flexibility of the
proposed method.